Green photosynthetic stems are often responsible for photosynthesis due to the reduction of leaves in arid and hot climates. We studied the response of PSII activity to high irradiance in the photosynthetic stems of Hexinia polydichotoma in the Taklimakan Desert by analysis of the fast fluorescence transients (OJIP). Leaf clips of a chlorophyll fluorometer were used in conjunction with a sponge with a 4-mm-width groove to prevent light leakage for precise in vivo measurements. High irradiance reduced performance indices, illustrating the photoinhibition of PSII to some extent. However, the decrease in active reaction centers (RC) per PSII absorption area and maximum quantum yield indicated a partial inactivation of RCs and an increase in excitation energy dissipation, resulting in downregulation of photosynthetic excitation pressure. In addition, the increased efficiency of electron transport to PSI acceptors alleviated overexcitation energy pressure on PSII. These mechanisms protected the PSII apparatus as well as PSI against damages from excessive excitation energy. We suggested that H. polydichotoma exhibited rather photoadaptation than photodamage when exposed to high irradiance during the summer in the Taklimakan Desert. The experiment also demonstrated that the modified leaf clip can be used for studying dark adaptation in a photosynthetic stem., L. Li, Z. Zhou, J. Liang, R. Lv., and Obsahuje seznam literatury
learning machine (ELM), as a new learning mechanism for single hidden layer feedforward neural networks (SLFNs), has shown its advantages, such as fast computation speed and good generalization performance. However, the weak robustness of ELM is an unavoidable defect for image classification. To address the problem, we propose a novel ensemble method which combines rotation forest and selective ensemble model in this paper. Firstly, ELM and rotation forest are integrated to construct an ensemble classifier (RF-ELM), which combines the advantages of both rotation forest and ELM. The purpose of rotation forest here is to enhance the diversity of each base classifier which can improve the performance generalization. Then several ELMs are removed from the ensemble pool by using genetic algorithm (GA) based selective ensemble model to further enhance the robustness. Finally, the remaining ELMs are grouped as a selected ensemble classifier (RFSEN-ELM) for image classification. The performance is analyzed and compared with several existing methods on benchmark datasets and the experimental results demonstrate that the proposed algorithm substantially improves the accuracy and robustness of classification at an acceptable level of training cost.